• 本节内容：像素的加减乘除

1. 两幅图像进行加减乘除时，图像类型、通道数、大小必须都一致。

C++

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main(int artc, char** argv) {
    Mat src1 = imread("D:/vcprojects/images/LinuxLogo.jpg");
    Mat src2 = imread("D:/vcprojects/images/WindowsLogo.jpg");
    if (src1.empty() || src2.empty()) {
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input", CV_WINDOW_AUTOSIZE);
    imshow("input1", src1);
    imshow("input2", src2);
    int height = src1.rows;
    int width = src1.cols;

    int b1 = 0, g1 = 0, r1 = 0;
    int b2 = 0, g2 = 0, r2 = 0;
    int b = 0, g = 0, r = 0;
    
    //自己实现加减乘除
    Mat result = Mat::zeros(src1.size(), src1.type());
    for (int row = 0; row < height; row++) {
        for (int col = 0; col < width; col++) {
                b1 = src1.at<Vec3b>(row, col)[0];//读出来是int类型
                g1 = src1.at<Vec3b>(row, col)[1];
                r1 = src1.at<Vec3b>(row, col)[2];

                b2 = src2.at<Vec3b>(row, col)[0];
                g2 = src2.at<Vec3b>(row, col)[1];
                r2 = src2.at<Vec3b>(row, col)[2];


//saturate_cast是C++函数，功能是实现精准的数据转型操作（损失很少精度，转型必然损失精度），如int转cchar、short转long等。建议对像素值转型时使用此函数。
//为什么要对像素值转型为uchar类型：读出来的b1和b2都是int型，若相加后不转型则可能大于255、相减后不转型可能大于0。RGB的像素值范围是[0,255]，不转型的话，无法用一个字节表示，会导致溢出。
//saturate_cast的作用：大于255时截断高位保留低位，认为是255；小于0时认为是0。
                result.at<Vec3b>(row, col)[0] = saturate_cast<uchar>(b1 + b2);
                result.at<Vec3b>(row, col)[1] = saturate_cast<uchar>(g1 + g2);//uchar是一个字节8位的
                result.at<Vec3b>(row, col)[2] = saturate_cast<uchar>(r1 + r2);
        }
    }
    imshow("output", result);


    //调用OpenCV的API完成加减乘除
    Mat add_result = Mat::zeros(src1.size(), src1.type()); //创建空白图像
    add(src1, src2, add_result); //add有三个参数（第一张图，第二张图，相加结果）。有时add有第四个参数，是mask。
    imshow("add_result", add_result);

    Mat sub_result = Mat::zeros(src1.size(), src1.type());
    subtract(src1, src2, sub_result);
    imshow("sub_result", sub_result);

    Mat mul_result = Mat::zeros(src1.size(), src1.type());
    multiply(src1, src2, mul_result);
    imshow("mul_result", mul_result);

    Mat div_result = Mat::zeros(src1.size(), src1.type());
    divide(src1, src2, div_result);
    imshow("div_result", div_result);
    
    waitKey(0);
    return 0;
}

Python

import cv2 as cv
import numpy as np

src1 = cv.imread("D:/vcprojects/images/LinuxLogo.jpg");
src2 = cv.imread("D:/vcprojects/images/WindowsLogo.jpg");
cv.imshow("input1", src1)
cv.imshow("input2", src2)
h, w, ch = src1.shape
print("h , w, ch", h, w, ch)

add_result = np.zeros(src1.shape, src1.dtype); # 创建全0图像，shape和data type与原图像相同
cv.add(src1, src2, add_result);
cv.imshow("add_result", add_result);

sub_result = np.zeros(src1.shape, src1.dtype);
cv.subtract(src1, src2, sub_result);
cv.imshow("sub_result", sub_result);

mul_result = np.zeros(src1.shape, src1.dtype);
cv.multiply(src1, src2, mul_result);
cv.imshow("mul_result", mul_result);

div_result = np.zeros(src1.shape, src1.dtype);
cv.divide(src1, src2, div_result);
cv.imshow("div_result", div_result);

cv.waitKey(0)
cv.destroyAllWindows()